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32
docs/en/datasets/pose/coco.md
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@ -14,12 +14,12 @@ The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialize
| Model | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ---------------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-pose.pt) | 640 | 50.4 | 80.1 | 131.8 | 1.18 | 3.3 | 9.2 |
| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-pose.pt) | 640 | 60.0 | 86.2 | 233.2 | 1.42 | 11.6 | 30.2 |
| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-pose.pt) | 640 | 65.0 | 88.8 | 456.3 | 2.00 | 26.4 | 81.0 |
| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-pose.pt) | 640 | 67.6 | 90.0 | 784.5 | 2.59 | 44.4 | 168.6 |
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-pose.pt) | 640 | 69.2 | 90.2 | 1607.1 | 3.73 | 69.4 | 263.2 |
| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-pose-p6.pt) | 1280 | 71.6 | 91.2 | 4088.7 | 10.04 | 99.1 | 1066.4 |
| [YOLO11n-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-pose.pt) | 640 | 50.4 | 80.1 | 131.8 | 1.18 | 3.3 | 9.2 |
| [YOLO11s-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-pose.pt) | 640 | 60.0 | 86.2 | 233.2 | 1.42 | 11.6 | 30.2 |
| [YOLO11m-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-pose.pt) | 640 | 65.0 | 88.8 | 456.3 | 2.00 | 26.4 | 81.0 |
| [YOLO11l-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-pose.pt) | 640 | 67.6 | 90.0 | 784.5 | 2.59 | 44.4 | 168.6 |
| [YOLO11x-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-pose.pt) | 640 | 69.2 | 90.2 | 1607.1 | 3.73 | 69.4 | 263.2 |
| [YOLO11x-pose-p6](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-pose-p6.pt) | 1280 | 71.6 | 91.2 | 4088.7 | 10.04 | 99.1 | 1066.4 |
## Key Features
@ -51,7 +51,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
## Usage
To train a YOLOv8n-pose model on the COCO-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train a YOLO11n-pose model on the COCO-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -61,7 +61,7 @@ To train a YOLOv8n-pose model on the COCO-Pose dataset for 100 [epochs](https://
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-pose.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="coco-pose.yaml", epochs=100, imgsz=640)
@ -71,7 +71,7 @@ To train a YOLOv8n-pose model on the COCO-Pose dataset for 100 [epochs](https://
```bash
# Start training from a pretrained *.pt model
yolo pose train data=coco-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
yolo pose train data=coco-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
```
## Sample Images and Annotations
@ -109,11 +109,11 @@ We would like to acknowledge the COCO Consortium for creating and maintaining th
### What is the COCO-Pose dataset and how is it used with Ultralytics YOLO for pose estimation?
The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialized version of the COCO (Common Objects in Context) dataset designed for pose estimation tasks. It builds upon the COCO Keypoints 2017 images and annotations, allowing for the training of models like Ultralytics YOLO for detailed pose estimation. For instance, you can use the COCO-Pose dataset to train a YOLOv8n-pose model by loading a pretrained model and training it with a YAML configuration. For training examples, refer to the [Training](../../modes/train.md) documentation.
The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialized version of the COCO (Common Objects in Context) dataset designed for pose estimation tasks. It builds upon the COCO Keypoints 2017 images and annotations, allowing for the training of models like Ultralytics YOLO for detailed pose estimation. For instance, you can use the COCO-Pose dataset to train a YOLO11n-pose model by loading a pretrained model and training it with a YAML configuration. For training examples, refer to the [Training](../../modes/train.md) documentation.
### How can I train a YOLOv8 model on the COCO-Pose dataset?
### How can I train a YOLO11 model on the COCO-Pose dataset?
Training a YOLOv8 model on the COCO-Pose dataset can be accomplished using either Python or CLI commands. For example, to train a YOLOv8n-pose model for 100 epochs with an image size of 640, you can follow the steps below:
Training a YOLO11 model on the COCO-Pose dataset can be accomplished using either Python or CLI commands. For example, to train a YOLO11n-pose model for 100 epochs with an image size of 640, you can follow the steps below:
!!! example "Train Example"
@ -123,7 +123,7 @@ Training a YOLOv8 model on the COCO-Pose dataset can be accomplished using eithe
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-pose.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="coco-pose.yaml", epochs=100, imgsz=640)
@ -133,14 +133,14 @@ Training a YOLOv8 model on the COCO-Pose dataset can be accomplished using eithe
```bash
# Start training from a pretrained *.pt model
yolo pose train data=coco-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
yolo pose train data=coco-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
```
For more details on the training process and available arguments, check the [training page](../../modes/train.md).
### What are the different metrics provided by the COCO-Pose dataset for evaluating model performance?
The COCO-Pose dataset provides several standardized evaluation metrics for pose estimation tasks, similar to the original COCO dataset. Key metrics include the Object Keypoint Similarity (OKS), which evaluates the [accuracy](https://www.ultralytics.com/glossary/accuracy) of predicted keypoints against ground truth annotations. These metrics allow for thorough performance comparisons between different models. For instance, the COCO-Pose pretrained models such as YOLOv8n-pose, YOLOv8s-pose, and others have specific performance metrics listed in the documentation, like mAP<sup>pose</sup>50-95 and mAP<sup>pose</sup>50.
The COCO-Pose dataset provides several standardized evaluation metrics for pose estimation tasks, similar to the original COCO dataset. Key metrics include the Object Keypoint Similarity (OKS), which evaluates the [accuracy](https://www.ultralytics.com/glossary/accuracy) of predicted keypoints against ground truth annotations. These metrics allow for thorough performance comparisons between different models. For instance, the COCO-Pose pretrained models such as YOLO11n-pose, YOLO11s-pose, and others have specific performance metrics listed in the documentation, like mAP<sup>pose</sup>50-95 and mAP<sup>pose</sup>50.
### How is the dataset structured and split for the COCO-Pose dataset?
@ -154,6 +154,6 @@ These subsets help organize the training, validation, and testing phases effecti
### What are the key features and applications of the COCO-Pose dataset?
The COCO-Pose dataset extends the COCO Keypoints 2017 annotations to include 17 keypoints for human figures, enabling detailed pose estimation. Standardized evaluation metrics (e.g., OKS) facilitate comparisons across different models. Applications of the COCO-Pose dataset span various domains, such as sports analytics, healthcare, and human-computer interaction, wherever detailed pose estimation of human figures is required. For practical use, leveraging pretrained models like those provided in the documentation (e.g., YOLOv8n-pose) can significantly streamline the process ([Key Features](#key-features)).
The COCO-Pose dataset extends the COCO Keypoints 2017 annotations to include 17 keypoints for human figures, enabling detailed pose estimation. Standardized evaluation metrics (e.g., OKS) facilitate comparisons across different models. Applications of the COCO-Pose dataset span various domains, such as sports analytics, healthcare, and human-computer interaction, wherever detailed pose estimation of human figures is required. For practical use, leveraging pretrained models like those provided in the documentation (e.g., YOLO11n-pose) can significantly streamline the process ([Key Features](#key-features)).
If you use the COCO-Pose dataset in your research or development work, please cite the paper with the following [BibTeX entry](#citations-and-acknowledgments).

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docs/en/datasets/pose/coco8-pose.md
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@ -1,7 +1,7 @@
---
comments: true
description: Explore the compact, versatile COCO8-Pose dataset for testing and debugging object detection models. Ideal for quick experiments with YOLOv8.
keywords: COCO8-Pose, Ultralytics, pose detection dataset, object detection, YOLOv8, machine learning, computer vision, training data
description: Explore the compact, versatile COCO8-Pose dataset for testing and debugging object detection models. Ideal for quick experiments with YOLO11.
keywords: COCO8-Pose, Ultralytics, pose detection dataset, object detection, YOLO11, machine learning, computer vision, training data
---
# COCO8-Pose Dataset
@ -10,7 +10,7 @@ keywords: COCO8-Pose, Ultralytics, pose detection dataset, object detection, YOL
[Ultralytics](https://www.ultralytics.com/) COCO8-Pose is a small, but versatile pose detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging [object detection](https://www.ultralytics.com/glossary/object-detection) models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
## Dataset YAML
@ -24,7 +24,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
## Usage
To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train a YOLO11n-pose model on the COCO8-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -34,7 +34,7 @@ To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 [epochs](https:/
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-pose.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
@ -44,7 +44,7 @@ To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 [epochs](https:/
```bash
# Start training from a pretrained *.pt model
yolo pose train data=coco8-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
yolo pose train data=coco8-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
```
## Sample Images and Annotations
@ -80,13 +80,13 @@ We would like to acknowledge the COCO Consortium for creating and maintaining th
## FAQ
### What is the COCO8-Pose dataset, and how is it used with Ultralytics YOLOv8?
### What is the COCO8-Pose dataset, and how is it used with Ultralytics YOLO11?
The COCO8-Pose dataset is a small, versatile pose detection dataset that includes the first 8 images from the COCO train 2017 set, with 4 images for training and 4 for validation. It's designed for testing and debugging object detection models and experimenting with new detection approaches. This dataset is ideal for quick experiments with [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/). For more details on dataset configuration, check out the dataset YAML file [here](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml).
The COCO8-Pose dataset is a small, versatile pose detection dataset that includes the first 8 images from the COCO train 2017 set, with 4 images for training and 4 for validation. It's designed for testing and debugging object detection models and experimenting with new detection approaches. This dataset is ideal for quick experiments with [Ultralytics YOLO11](https://docs.ultralytics.com/models/yolo11/). For more details on dataset configuration, check out the dataset YAML file [here](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml).
### How do I train a YOLOv8 model using the COCO8-Pose dataset in Ultralytics?
### How do I train a YOLO11 model using the COCO8-Pose dataset in Ultralytics?
To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an image size of 640, follow these examples:
To train a YOLO11n-pose model on the COCO8-Pose dataset for 100 epochs with an image size of 640, follow these examples:
!!! example "Train Example"
@ -96,7 +96,7 @@ To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an i
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-pose.pt")
model = YOLO("yolo11n-pose.pt")
# Train the model
results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
@ -105,7 +105,7 @@ To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an i
=== "CLI"
```bash
yolo pose train data=coco8-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
yolo pose train data=coco8-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
```
For a comprehensive list of training arguments, refer to the model [Training](../../modes/train.md) page.
@ -120,12 +120,12 @@ The COCO8-Pose dataset offers several benefits:
For more about its features and usage, see the [Dataset Introduction](#introduction) section.
### How does mosaicing benefit the YOLOv8 training process using the COCO8-Pose dataset?
### How does mosaicing benefit the YOLO11 training process using the COCO8-Pose dataset?
Mosaicing, demonstrated in the sample images of the COCO8-Pose dataset, combines multiple images into one, increasing the variety of objects and scenes within each training batch. This technique helps improve the model's ability to generalize across various object sizes, aspect ratios, and contexts, ultimately enhancing model performance. See the [Sample Images and Annotations](#sample-images-and-annotations) section for example images.
### Where can I find the COCO8-Pose dataset YAML file and how do I use it?
The COCO8-Pose dataset YAML file can be found [here](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml). This file defines the dataset configuration, including paths, classes, and other relevant information. Use this file with the YOLOv8 training scripts as mentioned in the [Train Example](#how-do-i-train-a-yolov8-model-using-the-coco8-pose-dataset-in-ultralytics) section.
The COCO8-Pose dataset YAML file can be found [here](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml). This file defines the dataset configuration, including paths, classes, and other relevant information. Use this file with the YOLO11 training scripts as mentioned in the [Train Example](#how-do-i-train-a-yolo11-model-using-the-coco8-pose-dataset-in-ultralytics) section.
For more FAQs and detailed documentation, visit the [Ultralytics Documentation](https://docs.ultralytics.com/).

20
docs/en/datasets/pose/hand-keypoints.md
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@ -8,7 +8,7 @@ keywords: Hand KeyPoints, pose estimation, dataset, keypoints, MediaPipe, YOLO,
## Introduction
The hand-keypoints dataset contains 26,768 images of hands annotated with keypoints, making it suitable for training models like Ultralytics YOLO for pose estimation tasks. The annotations were generated using the Google MediaPipe library, ensuring high accuracy and consistency, and the dataset is compatible [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) formats.
The hand-keypoints dataset contains 26,768 images of hands annotated with keypoints, making it suitable for training models like Ultralytics YOLO for pose estimation tasks. The annotations were generated using the Google MediaPipe library, ensuring high accuracy and consistency, and the dataset is compatible [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) formats.
## Hand Landmarks
@ -30,7 +30,7 @@ Each hand has a total of 21 keypoints.
## Key Features
- **Large Dataset**: 26,768 images with hand keypoint annotations.
- **YOLOv8 Compatibility**: Ready for use with YOLOv8 models.
- **YOLO11 Compatibility**: Ready for use with YOLO11 models.
- **21 Keypoints**: Detailed hand pose representation.
## Dataset Structure
@ -56,7 +56,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
## Usage
To train a YOLOv8n-pose model on the Hand Keypoints dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train a YOLO11n-pose model on the Hand Keypoints dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -66,7 +66,7 @@ To train a YOLOv8n-pose model on the Hand Keypoints dataset for 100 [epochs](htt
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-pose.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="hand-keypoints.yaml", epochs=100, imgsz=640)
@ -76,7 +76,7 @@ To train a YOLOv8n-pose model on the Hand Keypoints dataset for 100 [epochs](htt
```bash
# Start training from a pretrained *.pt model
yolo pose train data=hand-keypoints.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
yolo pose train data=hand-keypoints.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
```
## Sample Images and Annotations
@ -109,9 +109,9 @@ We would also like to acknowledge the creator of this dataset, [Rion Dsilva](htt
## FAQ
### How do I train a YOLOv8 model on the Hand Keypoints dataset?
### How do I train a YOLO11 model on the Hand Keypoints dataset?
To train a YOLOv8 model on the Hand Keypoints dataset, you can use either Python or the command line interface (CLI). Here's an example for training a YOLOv8n-pose model for 100 epochs with an image size of 640:
To train a YOLO11 model on the Hand Keypoints dataset, you can use either Python or the command line interface (CLI). Here's an example for training a YOLO11n-pose model for 100 epochs with an image size of 640:
!!! Example
@ -121,7 +121,7 @@ To train a YOLOv8 model on the Hand Keypoints dataset, you can use either Python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-pose.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="hand-keypoints.yaml", epochs=100, imgsz=640)
@ -131,7 +131,7 @@ To train a YOLOv8 model on the Hand Keypoints dataset, you can use either Python
```bash
# Start training from a pretrained *.pt model
yolo pose train data=hand-keypoints.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
yolo pose train data=hand-keypoints.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
```
For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
@ -141,7 +141,7 @@ For a comprehensive list of available arguments, refer to the model [Training](.
The Hand Keypoints dataset is designed for advanced pose estimation tasks and includes several key features:
- **Large Dataset**: Contains 26,768 images with hand keypoint annotations.
- **YOLOv8 Compatibility**: Ready for use with YOLOv8 models.
- **YOLO11 Compatibility**: Ready for use with YOLO11 models.
- **21 Keypoints**: Detailed hand pose representation, including wrist and finger joints.
For more details, you can explore the [Hand Keypoints Dataset](#introduction) section.

8
docs/en/datasets/pose/index.md
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@ -72,7 +72,7 @@ The `train` and `val` fields specify the paths to the directories containing the
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-pose.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
@ -82,7 +82,7 @@ The `train` and `val` fields specify the paths to the directories containing the
```bash
# Start training from a pretrained *.pt model
yolo pose train data=coco8-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
yolo pose train data=coco8-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
```
## Supported Datasets
@ -171,7 +171,7 @@ To use the COCO-Pose dataset with Ultralytics YOLO:
```python
from ultralytics import YOLO
model = YOLO("yolov8n-pose.pt") # load pretrained model
model = YOLO("yolo11n-pose.pt") # load pretrained model
results = model.train(data="coco-pose.yaml", epochs=100, imgsz=640)
```
@ -188,7 +188,7 @@ To add your dataset:
```python
from ultralytics import YOLO
model = YOLO("yolov8n-pose.pt")
model = YOLO("yolo11n-pose.pt")
results = model.train(data="your-dataset.yaml", epochs=100, imgsz=640)
```

28
docs/en/datasets/pose/tiger-pose.md
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@ -1,7 +1,7 @@
---
comments: true
description: Explore Ultralytics Tiger-Pose dataset with 263 diverse images. Ideal for testing, training, and refining pose estimation algorithms.
keywords: Ultralytics, Tiger-Pose, dataset, pose estimation, YOLOv8, training data, machine learning, neural networks
keywords: Ultralytics, Tiger-Pose, dataset, pose estimation, YOLO11, training data, machine learning, neural networks
---
# Tiger-Pose Dataset
@ -12,7 +12,7 @@ keywords: Ultralytics, Tiger-Pose, dataset, pose estimation, YOLOv8, training da
Despite its manageable size of 210 images, tiger-pose dataset offers diversity, making it suitable for assessing training pipelines, identifying potential errors, and serving as a valuable preliminary step before working with larger datasets for pose estimation.
This dataset is intended for use with [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
This dataset is intended for use with [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
<p align="center">
<br>
@ -22,7 +22,7 @@ This dataset is intended for use with [Ultralytics HUB](https://hub.ultralytics.
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Train YOLOv8 Pose Model on Tiger-Pose Dataset Using Ultralytics HUB
<strong>Watch:</strong> Train YOLO11 Pose Model on Tiger-Pose Dataset Using Ultralytics HUB
</p>
## Dataset YAML
@ -37,7 +37,7 @@ A YAML (Yet Another Markup Language) file serves as the means to specify the con
## Usage
To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
To train a YOLO11n-pose model on the Tiger-Pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
@ -47,7 +47,7 @@ To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 [epochs](https:/
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-pose.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="tiger-pose.yaml", epochs=100, imgsz=640)
@ -57,7 +57,7 @@ To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 [epochs](https:/
```bash
# Start training from a pretrained *.pt model
yolo task=pose mode=train data=tiger-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
yolo task=pose mode=train data=tiger-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
```
## Sample Images and Annotations
@ -101,11 +101,11 @@ The dataset has been released available under the [AGPL-3.0 License](https://git
### What is the Ultralytics Tiger-Pose dataset used for?
The Ultralytics Tiger-Pose dataset is designed for pose estimation tasks, consisting of 263 images sourced from a [YouTube video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0). The dataset is divided into 210 training images and 53 validation images. It is particularly useful for testing, training, and refining pose estimation algorithms using [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
The Ultralytics Tiger-Pose dataset is designed for pose estimation tasks, consisting of 263 images sourced from a [YouTube video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0). The dataset is divided into 210 training images and 53 validation images. It is particularly useful for testing, training, and refining pose estimation algorithms using [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics).
### How do I train a YOLOv8 model on the Tiger-Pose dataset?
### How do I train a YOLO11 model on the Tiger-Pose dataset?
To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an image size of 640, use the following code snippets. For more details, visit the [Training](../../modes/train.md) page:
To train a YOLO11n-pose model on the Tiger-Pose dataset for 100 epochs with an image size of 640, use the following code snippets. For more details, visit the [Training](../../modes/train.md) page:
!!! example "Train Example"
@ -115,7 +115,7 @@ To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an i
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-pose.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="tiger-pose.yaml", epochs=100, imgsz=640)
@ -126,16 +126,16 @@ To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an i
```bash
# Start training from a pretrained *.pt model
yolo task=pose mode=train data=tiger-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
yolo task=pose mode=train data=tiger-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
```
### What configurations does the `tiger-pose.yaml` file include?
The `tiger-pose.yaml` file is used to specify the configuration details of the Tiger-Pose dataset. It includes crucial data such as file paths and class definitions. To see the exact configuration, you can check out the [Ultralytics Tiger-Pose Dataset Configuration File](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/tiger-pose.yaml).
### How can I run inference using a YOLOv8 model trained on the Tiger-Pose dataset?
### How can I run inference using a YOLO11 model trained on the Tiger-Pose dataset?
To perform inference using a YOLOv8 model trained on the Tiger-Pose dataset, you can use the following code snippets. For a detailed guide, visit the [Prediction](../../modes/predict.md) page:
To perform inference using a YOLO11 model trained on the Tiger-Pose dataset, you can use the following code snippets. For a detailed guide, visit the [Prediction](../../modes/predict.md) page:
!!! example "Inference Example"
@ -161,4 +161,4 @@ To perform inference using a YOLOv8 model trained on the Tiger-Pose dataset, you
### What are the benefits of using the Tiger-Pose dataset for pose estimation?
The Tiger-Pose dataset, despite its manageable size of 210 images for training, provides a diverse collection of images that are ideal for testing pose estimation pipelines. The dataset helps identify potential errors and acts as a preliminary step before working with larger datasets. Additionally, the dataset supports the training and refinement of pose estimation algorithms using advanced tools like [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics), enhancing model performance and [accuracy](https://www.ultralytics.com/glossary/accuracy).
The Tiger-Pose dataset, despite its manageable size of 210 images for training, provides a diverse collection of images that are ideal for testing pose estimation pipelines. The dataset helps identify potential errors and acts as a preliminary step before working with larger datasets. Additionally, the dataset supports the training and refinement of pose estimation algorithms using advanced tools like [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLO11](https://github.com/ultralytics/ultralytics), enhancing model performance and [accuracy](https://www.ultralytics.com/glossary/accuracy).